Introduction

The Indian Premier League mostly known as IPL, is an Men's Twenty twenty cricket League founded in 2008, and held annually during March to May in India. The IPL is very popular among the cricket fans and youngsters. The total brand value of IPL in 2022 was Rs.90,038 and also it also have contributed to the GDP of the country (Source - wikipedia).

It is important to analyze the performance of each player and teams in IPL. Here we utlize the power of python to perform the exploratory data analysis to identify the most successful teams, players and most importantly to isolate the factors that have contributed to the win or loss of each team. Also this analysis aimed to suggest the players or teams that can help a company to endorse them for their products.

A total of 2 datasets (deliveries.csv and matches.csv) were used in this analysis. The deliveries and matches datasets have different features.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
%matplotlib inline

dfd=pd.read_csv("deliveries.csv")

dfd.head(100)

	match_id	inning	batting_team	bowling_team	over	ball	batsman	non_striker	bowler	is_super_over	...	bye_runs	legbye_runs	noball_runs	penalty_runs	batsman_runs	extra_runs	total_runs	player_dismissed	dismissal_kind	fielder
0	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	1	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	0	0	NaN	NaN	NaN
1	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	2	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	0	0	NaN	NaN	NaN
2	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	3	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	4	0	4	NaN	NaN	NaN
3	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	4	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	0	0	NaN	NaN	NaN
4	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	5	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	2	2	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	16	3	Yuvraj Singh	DJ Hooda	YS Chahal	0	...	0	0	0	0	1	0	1	NaN	NaN	NaN
96	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	16	4	DJ Hooda	Yuvraj Singh	YS Chahal	0	...	0	0	0	0	1	0	1	NaN	NaN	NaN
97	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	16	5	Yuvraj Singh	DJ Hooda	YS Chahal	0	...	0	0	0	0	1	0	1	NaN	NaN	NaN
98	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	16	6	DJ Hooda	Yuvraj Singh	YS Chahal	0	...	0	0	0	0	1	0	1	NaN	NaN	NaN
99	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	17	1	DJ Hooda	Yuvraj Singh	TS Mills	0	...	0	0	0	0	1	0	1	NaN	NaN	NaN

100 rows × 21 columns

dfd.shape

(179078, 21)

dfd.columns

Index(['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball',
       'batsman', 'non_striker', 'bowler', 'is_super_over', 'wide_runs',
       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs',
       'batsman_runs', 'extra_runs', 'total_runs', 'player_dismissed',
       'dismissal_kind', 'fielder'],
      dtype='object')

dfd.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 179078 entries, 0 to 179077
Data columns (total 21 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   match_id          179078 non-null  int64 
 1   inning            179078 non-null  int64 
 2   batting_team      179078 non-null  object
 3   bowling_team      179078 non-null  object
 4   over              179078 non-null  int64 
 5   ball              179078 non-null  int64 
 6   batsman           179078 non-null  object
 7   non_striker       179078 non-null  object
 8   bowler            179078 non-null  object
 9   is_super_over     179078 non-null  int64 
 10  wide_runs         179078 non-null  int64 
 11  bye_runs          179078 non-null  int64 
 12  legbye_runs       179078 non-null  int64 
 13  noball_runs       179078 non-null  int64 
 14  penalty_runs      179078 non-null  int64 
 15  batsman_runs      179078 non-null  int64 
 16  extra_runs        179078 non-null  int64 
 17  total_runs        179078 non-null  int64 
 18  player_dismissed  8834 non-null    object
 19  dismissal_kind    8834 non-null    object
 20  fielder           6448 non-null    object
dtypes: int64(13), object(8)
memory usage: 28.7+ MB

dfd.describe()

	match_id	inning	over	ball	is_super_over	wide_runs	bye_runs	legbye_runs	noball_runs	penalty_runs	batsman_runs	extra_runs	total_runs
count	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000	179078.000000
mean	1802.252957	1.482952	10.162488	3.615587	0.000452	0.036721	0.004936	0.021136	0.004183	0.000056	1.246864	0.067032	1.313897
std	3472.322805	0.502074	5.677684	1.806966	0.021263	0.251161	0.116480	0.194908	0.070492	0.016709	1.608270	0.342553	1.605422
min	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	190.000000	1.000000	5.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	379.000000	1.000000	10.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	1.000000
75%	567.000000	2.000000	15.000000	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	1.000000
max	11415.000000	5.000000	20.000000	9.000000	1.000000	5.000000	4.000000	5.000000	5.000000	5.000000	7.000000	7.000000	10.000000

dfd.isnull().sum()

match_id                 0
inning                   0
batting_team             0
bowling_team             0
over                     0
ball                     0
batsman                  0
non_striker              0
bowler                   0
is_super_over            0
wide_runs                0
bye_runs                 0
legbye_runs              0
noball_runs              0
penalty_runs             0
batsman_runs             0
extra_runs               0
total_runs               0
player_dismissed    170244
dismissal_kind      170244
fielder             172630
dtype: int64

dfd["player_dismissed"].value_counts()

SK Raina      162
RG Sharma     155
RV Uthappa    153
V Kohli       143
S Dhawan      137
             ... 
S Randiv        1
R Ninan         1
B Sumanth       1
C Madan         1
S Singh         1
Name: player_dismissed, Length: 487, dtype: int64

Some Observations from the deliveries dataset: > A total of 179078 rows and 21 columns are present. > The values in the columns viz., player_dismissed,dismissal_kind and fielder have null values, while all other columns have non null values > The columns such as batting team,bowling team,batsman, nonsticker, bowler, player_dismissed, dismissal kind and fielder have obect data types, while all other columns have int64 datatype > Maximum balls thrown in an over is 9. > Maximum runs scored in an over is 10.

dfm=pd.read_csv("matches.csv")

dfm.head(40)

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2	umpire3
0	1	2017	Hyderabad	2017-04-05	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong	NaN
1	2	2017	Pune	2017-04-06	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NaN
2	3	2017	Rajkot	2017-04-07	Gujarat Lions	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Kolkata Knight Riders	0	10	CA Lynn	Saurashtra Cricket Association Stadium	Nitin Menon	CK Nandan	NaN
3	4	2017	Indore	2017-04-08	Rising Pune Supergiant	Kings XI Punjab	Kings XI Punjab	field	normal	0	Kings XI Punjab	0	6	GJ Maxwell	Holkar Cricket Stadium	AK Chaudhary	C Shamshuddin	NaN
4	5	2017	Bangalore	2017-04-08	Royal Challengers Bangalore	Delhi Daredevils	Royal Challengers Bangalore	bat	normal	0	Royal Challengers Bangalore	15	0	KM Jadhav	M Chinnaswamy Stadium	NaN	NaN	NaN
5	6	2017	Hyderabad	2017-04-09	Gujarat Lions	Sunrisers Hyderabad	Sunrisers Hyderabad	field	normal	0	Sunrisers Hyderabad	0	9	Rashid Khan	Rajiv Gandhi International Stadium, Uppal	A Deshmukh	NJ Llong	NaN
6	7	2017	Mumbai	2017-04-09	Kolkata Knight Riders	Mumbai Indians	Mumbai Indians	field	normal	0	Mumbai Indians	0	4	N Rana	Wankhede Stadium	Nitin Menon	CK Nandan	NaN
7	8	2017	Indore	2017-04-10	Royal Challengers Bangalore	Kings XI Punjab	Royal Challengers Bangalore	bat	normal	0	Kings XI Punjab	0	8	AR Patel	Holkar Cricket Stadium	AK Chaudhary	C Shamshuddin	NaN
8	9	2017	Pune	2017-04-11	Delhi Daredevils	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Delhi Daredevils	97	0	SV Samson	Maharashtra Cricket Association Stadium	AY Dandekar	S Ravi	NaN
9	10	2017	Mumbai	2017-04-12	Sunrisers Hyderabad	Mumbai Indians	Mumbai Indians	field	normal	0	Mumbai Indians	0	4	JJ Bumrah	Wankhede Stadium	Nitin Menon	CK Nandan	NaN
10	11	2017	Kolkata	2017-04-13	Kings XI Punjab	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Kolkata Knight Riders	0	8	SP Narine	Eden Gardens	A Deshmukh	NJ Llong	NaN
11	12	2017	Bangalore	2017-04-14	Royal Challengers Bangalore	Mumbai Indians	Mumbai Indians	field	normal	0	Mumbai Indians	0	4	KA Pollard	M Chinnaswamy Stadium	KN Ananthapadmanabhan	AK Chaudhary	NaN
12	13	2017	Rajkot	2017-04-14	Rising Pune Supergiant	Gujarat Lions	Gujarat Lions	field	normal	0	Gujarat Lions	0	7	AJ Tye	Saurashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NaN
13	14	2017	Kolkata	2017-04-15	Kolkata Knight Riders	Sunrisers Hyderabad	Sunrisers Hyderabad	field	normal	0	Kolkata Knight Riders	17	0	RV Uthappa	Eden Gardens	AY Dandekar	NJ Llong	NaN
14	15	2017	Delhi	2017-04-15	Delhi Daredevils	Kings XI Punjab	Delhi Daredevils	bat	normal	0	Delhi Daredevils	51	0	CJ Anderson	Feroz Shah Kotla	YC Barde	Nitin Menon	NaN
15	16	2017	Mumbai	2017-04-16	Gujarat Lions	Mumbai Indians	Mumbai Indians	field	normal	0	Mumbai Indians	0	6	N Rana	Wankhede Stadium	A Nand Kishore	S Ravi	NaN
16	17	2017	Bangalore	2017-04-16	Rising Pune Supergiant	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Rising Pune Supergiant	27	0	BA Stokes	M Chinnaswamy Stadium	KN Ananthapadmanabhan	C Shamshuddin	NaN
17	18	2017	Delhi	2017-04-17	Delhi Daredevils	Kolkata Knight Riders	Delhi Daredevils	bat	normal	0	Kolkata Knight Riders	0	4	NM Coulter-Nile	Feroz Shah Kotla	Nitin Menon	CK Nandan	NaN
18	19	2017	Hyderabad	2017-04-17	Sunrisers Hyderabad	Kings XI Punjab	Kings XI Punjab	field	normal	0	Sunrisers Hyderabad	5	0	B Kumar	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	A Deshmukh	NaN
19	20	2017	Rajkot	2017-04-18	Royal Challengers Bangalore	Gujarat Lions	Gujarat Lions	field	normal	0	Royal Challengers Bangalore	21	0	CH Gayle	Saurashtra Cricket Association Stadium	S Ravi	VK Sharma	NaN
20	21	2017	Hyderabad	2017-04-19	Sunrisers Hyderabad	Delhi Daredevils	Sunrisers Hyderabad	bat	normal	0	Sunrisers Hyderabad	15	0	KS Williamson	Rajiv Gandhi International Stadium, Uppal	CB Gaffaney	NJ Llong	NaN
21	22	2017	Indore	2017-04-20	Kings XI Punjab	Mumbai Indians	Mumbai Indians	field	normal	0	Mumbai Indians	0	8	JC Buttler	Holkar Cricket Stadium	M Erasmus	C Shamshuddin	NaN
22	23	2017	Kolkata	2017-04-21	Kolkata Knight Riders	Gujarat Lions	Gujarat Lions	field	normal	0	Gujarat Lions	0	4	SK Raina	Eden Gardens	CB Gaffaney	Nitin Menon	NaN
23	24	2017	Mumbai	2017-04-22	Mumbai Indians	Delhi Daredevils	Delhi Daredevils	field	normal	0	Mumbai Indians	14	0	MJ McClenaghan	Wankhede Stadium	A Nand Kishore	S Ravi	NaN
24	25	2017	Pune	2017-04-22	Sunrisers Hyderabad	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	6	MS Dhoni	Maharashtra Cricket Association Stadium	AY Dandekar	A Deshmukh	NaN
25	26	2017	Rajkot	2017-04-23	Kings XI Punjab	Gujarat Lions	Gujarat Lions	field	normal	0	Kings XI Punjab	26	0	HM Amla	Saurashtra Cricket Association Stadium	AK Chaudhary	M Erasmus	NaN
26	27	2017	Kolkata	2017-04-23	Kolkata Knight Riders	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Kolkata Knight Riders	82	0	NM Coulter-Nile	Eden Gardens	CB Gaffaney	CK Nandan	NaN
27	28	2017	Mumbai	2017-04-24	Rising Pune Supergiant	Mumbai Indians	Mumbai Indians	field	normal	0	Rising Pune Supergiant	3	0	BA Stokes	Wankhede Stadium	A Nand Kishore	S Ravi	NaN
28	29	2017	Pune	2017-04-26	Rising Pune Supergiant	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Kolkata Knight Riders	0	7	RV Uthappa	Maharashtra Cricket Association Stadium	AY Dandekar	NJ Llong	NaN
29	30	2017	Bangalore	2017-04-27	Royal Challengers Bangalore	Gujarat Lions	Gujarat Lions	field	normal	0	Gujarat Lions	0	7	AJ Tye	M Chinnaswamy Stadium	AK Chaudhary	C Shamshuddin	NaN
30	31	2017	Kolkata	2017-04-28	Delhi Daredevils	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Kolkata Knight Riders	0	7	G Gambhir	Eden Gardens	NJ Llong	S Ravi	NaN
31	32	2017	Chandigarh	2017-04-28	Sunrisers Hyderabad	Kings XI Punjab	Kings XI Punjab	field	normal	0	Sunrisers Hyderabad	26	0	Rashid Khan	Punjab Cricket Association IS Bindra Stadium, ...	Nitin Menon	CK Nandan	NaN
32	33	2017	Pune	2017-04-29	Rising Pune Supergiant	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Rising Pune Supergiant	61	0	LH Ferguson	Maharashtra Cricket Association Stadium	KN Ananthapadmanabhan	M Erasmus	NaN
33	34	2017	Rajkot	2017-04-29	Gujarat Lions	Mumbai Indians	Gujarat Lions	bat	tie	0	Mumbai Indians	0	0	KH Pandya	Saurashtra Cricket Association Stadium	AK Chaudhary	CB Gaffaney	NaN
34	35	2017	Chandigarh	2017-04-30	Delhi Daredevils	Kings XI Punjab	Kings XI Punjab	field	normal	0	Kings XI Punjab	0	10	Sandeep Sharma	Punjab Cricket Association IS Bindra Stadium, ...	YC Barde	CK Nandan	NaN
35	36	2017	Hyderabad	2017-04-30	Sunrisers Hyderabad	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Sunrisers Hyderabad	48	0	DA Warner	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	S Ravi	NaN
36	37	2017	Mumbai	2017-05-01	Royal Challengers Bangalore	Mumbai Indians	Royal Challengers Bangalore	bat	normal	0	Mumbai Indians	0	5	RG Sharma	Wankhede Stadium	AK Chaudhary	CB Gaffaney	NaN
37	38	2017	Pune	2017-05-01	Gujarat Lions	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	5	BA Stokes	Maharashtra Cricket Association Stadium	M Erasmus	C Shamshuddin	NaN
38	39	2017	Delhi	2017-05-02	Sunrisers Hyderabad	Delhi Daredevils	Delhi Daredevils	field	normal	0	Delhi Daredevils	0	6	Mohammed Shami	Feroz Shah Kotla	YC Barde	Nitin Menon	NaN
39	40	2017	Kolkata	2017-05-03	Kolkata Knight Riders	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	4	RA Tripathi	Eden Gardens	KN Ananthapadmanabhan	A Nand Kishore	NaN

dfm.shape

(756, 18)

dfm.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 756 entries, 0 to 755
Data columns (total 18 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   id               756 non-null    int64 
 1   season           756 non-null    int64 
 2   city             749 non-null    object
 3   date             756 non-null    object
 4   team1            756 non-null    object
 5   team2            756 non-null    object
 6   toss_winner      756 non-null    object
 7   toss_decision    756 non-null    object
 8   result           756 non-null    object
 9   dl_applied       756 non-null    int64 
 10  winner           752 non-null    object
 11  win_by_runs      756 non-null    int64 
 12  win_by_wickets   756 non-null    int64 
 13  player_of_match  752 non-null    object
 14  venue            756 non-null    object
 15  umpire1          754 non-null    object
 16  umpire2          754 non-null    object
 17  umpire3          119 non-null    object
dtypes: int64(5), object(13)
memory usage: 106.4+ KB

dfm.describe()

	id	season	dl_applied	win_by_runs	win_by_wickets
count	756.000000	756.000000	756.000000	756.000000	756.000000
mean	1792.178571	2013.444444	0.025132	13.283069	3.350529
std	3464.478148	3.366895	0.156630	23.471144	3.387963
min	1.000000	2008.000000	0.000000	0.000000	0.000000
25%	189.750000	2011.000000	0.000000	0.000000	0.000000
50%	378.500000	2013.000000	0.000000	0.000000	4.000000
75%	567.250000	2016.000000	0.000000	19.000000	6.000000
max	11415.000000	2019.000000	1.000000	146.000000	10.000000

Some observations from matches dataset: > The matches dataset have 756 rows and 18 columns > The columns id,season,dl_applied, win_by_runs and win_by_wickets are int64 datatype, while other columns are objects. > The maximum runs by which a team won is 146 and maximum wickets a team won is 10.

dfm.isnull().sum()

id                   0
season               0
city                 7
date                 0
team1                0
team2                0
toss_winner          0
toss_decision        0
result               0
dl_applied           0
winner               4
win_by_runs          0
win_by_wickets       0
player_of_match      4
venue                0
umpire1              2
umpire2              2
umpire3            637
dtype: int64

dfm['umpire3'].unique()

array([nan, 'Anil Chaudhary', 'Nitin Menon', 'S Ravi', 'O Nandan',
       'A Nanda Kishore', 'Vineet Kulkarni', 'C Shamshuddin',
       'Rod Tucker', 'Chris Gaffaney', 'A.D Deshmukh', 'Nigel Llong',
       'K Ananthapadmanabhan', 'Anil Dandekar', 'Virender Kumar Sharma',
       'Yeshwant Barde', 'Bruce Oxenford', 'Marais Erasmus',
       'Kumar Dharmasena', 'KN Anantapadmanabhan', 'Ulhas Gandhe',
       'Nanda Kishore', 'Ian Gould', 'Sundaram Ravi',
       'KN Ananthapadmanabhan', 'Chettithody Shamshuddin'], dtype=object)

dfd["inning"].unique()

array([1, 2, 3, 4, 5], dtype=int64)

dfd['total_runs'].unique()

array([ 0,  4,  2,  1,  6,  3,  5,  7, 10,  8], dtype=int64)

dfd['total_runs'].sum()

235290

dept_att=dfd.groupby(['batting_team']).apply(lambda x:x['total_runs'].sum()).reset_index(name='Total Runs').sort_values(by="Total Runs", ascending=False)
fig=px.bar(dept_att,x='batting_team',y='Total Runs',text_auto='.2s',title='Distribution of Total Runs by Batting team for a period of 2008 to 2019')
fig.update_layout(yaxis_range=[0,35000])
fig.show()

data_match=dfm.groupby(["winner"]).apply(lambda x:x["winner"].count()).reset_index(name="Total Matches").sort_values(by="Total Matches",ascending=False)
fig=px.bar(data_match,x="winner",y="Total Matches",text_auto='.2s',title="Disturbution of Matches Played by Teams between 2008 to 2019")
fig.show()

dfd['match_id'].value_counts().sort_values()

11340     51
301       63
452      100
568      106
120      108
        ... 
388      261
476      262
534      262
34       263
126      267
Name: match_id, Length: 756, dtype: int64

data_batsman=dfd.groupby(["batsman","batting_team"]).apply(lambda x:x["total_runs"].sum()).reset_index(name="Total Runs").sort_values(by="Total Runs",ascending=False).head(10)
figu=px.bar(data_batsman,x="batsman",y="Total Runs",color="batting_team",text_auto=".2s",title="Top 10 Batsman of IPL Teams by Total Runs between the period of 2008 to 2019")
figu.update_layout(yaxis_range=[0,6000])
figu.show()

data_bowl=dfd.groupby(["bowler","bowling_team"]).apply(lambda x:x["player_dismissed"].count()).reset_index(name="Total Wickets").sort_values(by="Total Wickets",ascending=False).head(10)
figud=px.bar(data_bowl,x="bowler",y="Total Wickets",color="bowling_team",title="Top 10 bowlers of IPL Teams with most wickets between the perdiod of 2008 to 2019")
figud.update_layout(yaxis_range=[0,200])
figud.show()

data_wide=dfd.groupby(['bowler']).apply(lambda x:x['wide_runs'].count()).reset_index(name="Most Wides").sort_values(by="Most Wides",ascending=False).head(5)
fig_wid=px.pie(data_wide,values="Most Wides",names="bowler",title="Top 5 bowlers with most Wides for IPL Teams for a period of 2008 to 2019")
fig_wid.show()

data_dism=dfd.groupby(['dismissal_kind']).apply(lambda x:x['dismissal_kind'].count()).reset_index(name="Top 5 Dismissal Type").sort_values(by="Top 5 Dismissal Type",ascending=False).head(5)
fig_dism=px.pie(data_dism,values="Top 5 Dismissal Type",names="dismissal_kind",title="Top 5 Dismissal Types of IPL Teams for  period between 2008 to 2019")
fig_dism.show()

dfd["dismissal_kind"].unique()

array([nan, 'caught', 'bowled', 'run out', 'lbw', 'caught and bowled',
       'stumped', 'retired hurt', 'hit wicket', 'obstructing the field'],
      dtype=object)

data_noball=dfd.groupby(['bowler']).apply(lambda x:x['noball_runs'].count()).reset_index(name="Top 5 Bowlers with Most No Balls").sort_values(by="Top 5 Bowlers with Most No Balls").head(5)
fig_noball=px.pie(data_noball,values="Top 5 Bowlers with Most No Balls",names="bowler",title="Top 5 Bowlers with Most No Ball in IPL Teams for a perdiod of 2008 to 2019")
fig_noball.show()

dfm["date"]=dfm["date"].apply(lambda x: pd.to_datetime(x).strftime("%Y-%m-%d"))

plt.figure(figsize=(20,8))
axx =sns.lineplot(x="season",y="win_by_runs",data=dfm,ci=None,palette="YlGnBu_r",lw=2)
axx.set(xlabel="Season",ylabel="Average win by Runs",title="Average win by Runs through Season")
plt.show()

dfm

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2	umpire3
0	1	2017	Hyderabad	2017-04-05	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong	NaN
1	2	2017	Pune	2017-04-06	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NaN
2	3	2017	Rajkot	2017-04-07	Gujarat Lions	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Kolkata Knight Riders	0	10	CA Lynn	Saurashtra Cricket Association Stadium	Nitin Menon	CK Nandan	NaN
3	4	2017	Indore	2017-04-08	Rising Pune Supergiant	Kings XI Punjab	Kings XI Punjab	field	normal	0	Kings XI Punjab	0	6	GJ Maxwell	Holkar Cricket Stadium	AK Chaudhary	C Shamshuddin	NaN
4	5	2017	Bangalore	2017-04-08	Royal Challengers Bangalore	Delhi Daredevils	Royal Challengers Bangalore	bat	normal	0	Royal Challengers Bangalore	15	0	KM Jadhav	M Chinnaswamy Stadium	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
751	11347	2019	Mumbai	2019-05-05	Kolkata Knight Riders	Mumbai Indians	Mumbai Indians	field	normal	0	Mumbai Indians	0	9	HH Pandya	Wankhede Stadium	Nanda Kishore	O Nandan	S Ravi
752	11412	2019	Chennai	2019-07-05	Chennai Super Kings	Mumbai Indians	Chennai Super Kings	bat	normal	0	Mumbai Indians	0	6	AS Yadav	M. A. Chidambaram Stadium	Nigel Llong	Nitin Menon	Ian Gould
753	11413	2019	Visakhapatnam	2019-08-05	Sunrisers Hyderabad	Delhi Capitals	Delhi Capitals	field	normal	0	Delhi Capitals	0	2	RR Pant	ACA-VDCA Stadium	NaN	NaN	NaN
754	11414	2019	Visakhapatnam	2019-10-05	Delhi Capitals	Chennai Super Kings	Chennai Super Kings	field	normal	0	Chennai Super Kings	0	6	F du Plessis	ACA-VDCA Stadium	Sundaram Ravi	Bruce Oxenford	Chettithody Shamshuddin
755	11415	2019	Hyderabad	2019-12-05	Mumbai Indians	Chennai Super Kings	Mumbai Indians	bat	normal	0	Mumbai Indians	1	0	JJ Bumrah	Rajiv Gandhi Intl. Cricket Stadium	Nitin Menon	Ian Gould	Nigel Llong

756 rows × 18 columns

plt.figure(figsize=(20,8))
axy=sns.lineplot(x="season",y="win_by_runs",hue="winner",data=dfm,ci=None,lw=2)
axy.set(xlabel="season",ylabel="Average win by Runs",title="Average win by Runs with Season and Teams")

[Text(0.5, 0, 'season'),
 Text(0, 0.5, 'Average win by Runs'),
 Text(0.5, 1.0, 'Average win by Runs with Season and Teams')]

import squarify

ndf=dfm.groupby('winner')[['win_by_runs']].mean().sort_values(by="win_by_runs",ascending=False)

ndf

	win_by_runs
winner
Chennai Super Kings	17.780000
Mumbai Indians	17.119266
Royal Challengers Bangalore	14.904762
Deccan Chargers	14.517241
Rising Pune Supergiant	12.300000
Sunrisers Hyderabad	12.293103
Rajasthan Royals	11.933333
Kolkata Knight Riders	11.804348
Pune Warriors	11.583333
Kings XI Punjab	11.280488
Rising Pune Supergiants	10.600000
Delhi Daredevils	10.074627
Delhi Capitals	9.200000
Kochi Tuskers Kerala	3.833333
Gujarat Lions	0.076923

win=dfm.groupby('winner')[['win_by_runs']].mean().index.get_level_values(0).tolist()
print(win)

['Chennai Super Kings', 'Deccan Chargers', 'Delhi Capitals', 'Delhi Daredevils', 'Gujarat Lions', 'Kings XI Punjab', 'Kochi Tuskers Kerala', 'Kolkata Knight Riders', 'Mumbai Indians', 'Pune Warriors', 'Rajasthan Royals', 'Rising Pune Supergiant', 'Rising Pune Supergiants', 'Royal Challengers Bangalore', 'Sunrisers Hyderabad']

wbr=dfm.groupby('winner')[['win_by_runs']].mean().reset_index().win_by_runs.values.tolist()
print(wbr)

[17.78, 14.517241379310345, 9.2, 10.074626865671641, 0.07692307692307693, 11.28048780487805, 3.8333333333333335, 11.804347826086957, 17.119266055045873, 11.583333333333334, 11.933333333333334, 12.3, 10.6, 14.904761904761905, 12.293103448275861]

plt.figure(figsize=(20,12))
squarify.plot(sizes=wbr,label=win,alpha=0.8)
plt.axis('off')
plt.show()

df_uc=dfm.copy()

data_twin=dfm.groupby(["winner","season"]).apply(lambda x:x['winner'].count()).reset_index(name="Team with most Wins").sort_values(by="Team with most Wins",ascending=False)
fig_twin=px.bar(data_twin,x="season",y="Team with most Wins",text_auto = '.2s',color = "winner", title="IPL Teams with most win over the Years for a period of 2008 to 2019")
fig_twin.show()

dfm['city'].nunique()

32

top_city=dfm.groupby(['city']).apply(lambda x:x['city'].count()).reset_index(name="Cities Count").sort_values(by="Cities Count",ascending=False)
fig_topcity=px.bar(top_city,x="city",y="Cities Count",text_auto='.2s',title="Cities with Most IPL Matches conducted for a period of 2008 to 2019")
fig_topcity.show()

top_team=dfm.groupby(['winner']).apply(lambda x:x['winner'].count()).reset_index(name="Winner Count").sort_values(by="Winner Count",ascending=False)
fig_topcity=px.bar(top_team,x="winner",y="Winner Count",text_auto='.2s',title="IPL Teams with Most Wins for a period of 2008 to 2019")
fig_topcity.show()

# top 5 players of the match
top_playmatch=dfm.groupby(['player_of_match','season']).apply(lambda x:x['player_of_match'].count()).reset_index(name="IPL Top Player of Matches").sort_values(by="IPL Top Player of Matches",ascending=False).head(10)
fig_topp=px.bar(top_playmatch,x="player_of_match",y="IPL Top Player of Matches",color="season",text_auto=".2s",title="IPL Teams player of the Match with season")
fig_topp.show()

# top 5 players of the match
top_playmatch=dfm.groupby(['player_of_match','winner']).apply(lambda x:x['player_of_match'].count()).reset_index(name="Winner with Most Man of Matches").sort_values(by="Winner with Most Man of Matches",ascending=False).head(15)
fig_topp=px.bar(top_playmatch,x="player_of_match",y="Winner with Most Man of Matches",color="winner",text_auto=".2s",title="IPL Teams with Most Man of Match Awards")
fig_topp.show()

#top 5 players of the match
top_stadium=dfm.groupby(['venue','season']).apply(lambda x:x['venue'].count()).reset_index(name="Stadium with Most Matches").sort_values(by="Stadium with Most Matches",ascending=False).head(10)
figg_topp=px.bar(top_stadium,x="venue",y="Stadium with Most Matches",color="season",text_auto=".2s",title="Venue(Stadium) with Most IPL Matches")
figg_topp.show()

#top 5 players of the match
top_stadium=dfm.groupby(['venue','winner']).apply(lambda x:x['venue'].count()).reset_index(name="Stadium with Most Matches").sort_values(by="Stadium with Most Matches",ascending=False).head(10)
figg_topp=px.bar(top_stadium,x="venue",y="Stadium with Most Matches",color="winner",text_auto=".2s",title="Top 10 Stadiums with Most Wins by IPL Teams between 2008 to 2019")
figg_topp.show()

Insights from this Exploratory data Analysis on IPL teams

# These insights is for IPL teams that played between the period of 2008 to 2019.The following are the insights abtained from the above analysis, that might be helpful to make data based decision making. > The Mumbai Indians IPL team scored the highest runs compared to all other teams, followed by Royal Challengers Banglaore. > The Mumbai Indians played the highest total matches (110) with the winning outcome. The second higher matchs played with winning outcome is of chennai superkings (100). > Virat Kohli of Royal Challenges Bangalore scored the highest runs (5616) followed by Suresh K Raina of Chennai super kings scored the next highest runs (4779). > S L Maliga of Mumai indian IPL team is top bowler with highest wicket of 188. Also S K Naire of kolkota knight riders is the second follower in top bowlers list with a total wicket of 137. > Harbajan Singh of Mumbai indian is the top bowler with most wide balls of 3451 wides. > Out of the total dismissals happened in the IPL matches for these mentioned period of 2008 to 2019 the top way (62.2%) of dismissal of a player is through a catch. > S A Yadav and B Chipli are the bowlers with most wide balls in the IPL matches happened between 2008 to 2019. > The average win by runs of chennai super kings is 17.78 and that of Mumbai indians is 17.11 > Rajasthan royals have the highest win of 13 in the year 2008 and Mumbai Indians have a total win of 11 in year 2019. > Mumbai is the city with most IPL matches (100) happened and Kolkota hosted a total of 77 matches. > Mumabai indians have the highest winning match history with 109 wins. > CH Gayle have won the man of the match awards around total 11 with 6 times in 2011 and 5 times in the year 2012. > AB de williams won the man of the match awards most times with of total of 18. > The important factor which contributed for the higher win of Mumbai indian is because of most of matches where, Mumbai indians won happened at Wankhede stadium in Mumbai. > The Mumbai indian team can be endorsed by a company for their products. Also Virat Kohi, S L maliga, C H Gayle, AB de williams can also be endrosed by the company as brand ambassdors, due to their higher acheivement and popularity in the entire theme of IPL seasons.